However, similar to the literature on adaptive dynamics [1]}, [2]}, we focus on players who adapt locally. This means that in each updating step, learners make only minor modifications to their existing strategies. In that case, two selfish learners may get stuck in rest points that are locally stable without being Nash equilibria. Local adaptation leads to “smoother” trajectories of the learning dynamics. However, as shown by Stewart and Plotkin [3]}, the qualitative outcomes are comparable to those of global updating. Again, eventually players either mutually cooperate, defect, alternate, or they employ equalizer strategies. However, simulations suggest that they become even more likely to settle at mutual defection [3]}. Selfish learning is therefore particularly prone to result in inefficient outcomes under local adaptation.
